Strong evolutionary convergence of receptor-binding protein spike between COVID-19 and SARS-related coronaviruses

  1. Yonghua Wu

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Abstract

Coronavirus Disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS)-related coronaviruses (e.g., 2019-nCoV and SARS-CoV) are phylogenetically distantly related, but both are capable of infecting human hosts via the same receptor, angiotensin-converting enzyme 2, and cause similar clinical and pathological features, suggesting their phenotypic convergence. Yet, the molecular basis that underlies their phenotypic convergence remains unknown. Here, we used a recently developed molecular phyloecological approach to examine the molecular basis leading to their phenotypic convergence. Our genome-level analyses show that the spike protein, which is responsible for receptor binding, has undergone significant Darwinian selection along the branches related to 2019-nCoV and SARS-CoV. Further examination shows an unusually high proportion of evolutionary convergent amino acid sites in the receptor binding domain (RBD) of the spike protein between COVID-19 and SARS-related CoV clades, leading to the phylogenetic uniting of their RBD protein sequences. In addition to the spike protein, we also find the evolutionary convergence of its partner protein, ORF3a , suggesting their possible co-evolutionary convergence. Our results demonstrate a strong adaptive evolutionary convergence between COVID-19 and SARS-related CoV, possibly facilitating their adaptation to similar or identical receptors. Finally, it should be noted that many observed bat SARS-like CoVs that have an evolutionary convergent RBD sequence with 2019-nCoV and SARS-CoV may be pre-adapted to human host receptor ACE2, and hence would be potential new coronavirus sources to infect humans in the future.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.04.975995: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We aligned these genome sequences using MAFFT (https://mafft.cbrc.jp/alignment/server/).
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We aligned these homologous gene sequences using the online software webPRANK (http://www.ebi.ac.uk/goldman-srv/webprank/)38, which is considered to create a more reliable alignment to decrease false-positive results in positive selection analyses39.
    webPRANK
    suggested: (prank, RRID:SCR_017228)
    Selection intensity analyses: We analyzed relative selection intensity using the RELAX19 program, available from the Datamonkey webserver (http://test.datamonkey.org/relax).
    Datamonkey
    suggested: (DataMonkey, RRID:SCR_010278)
    Phylogenetic analyses: We reconstructed an maximum likelihood (ML) tree and neighbor-joining (NJ) tree using MEGA X40.
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    The RBD amino acid sequences of our focal 35 coronavirus genomes were abstracted and aligned using CLUSTAL W41 program.
    CLUSTAL
    suggested: (Clustal X , RRID:SCR_017055)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. ScreenIT

    SciScore for 10.1101/2020.03.04.975995: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We aligned these genome sequences using MAFFT (https://mafft.cbrc.jp/alignment/server/).
    MAFFT
    suggested: (MAFFT, SCR_011811)
    We aligned these homologous gene sequences using the online software webPRANK (http://www.ebi.ac.uk/goldman-srv/webprank/) 38, which is considered to create a more reliable alignment to decrease false-positive results in positive selection analyses 39.
    webPRANK
    suggested: (prank, SCR_017228)
    Selection intensity analyses We analyzed relative selection intensity using the RELAX 19 program, available from the Datamonkey webserver (http://test.datamonkey.org/relax).
    Datamonkey
    suggested: (DataMonkey, SCR_010278)
    Phylogenetic analyses We reconstructed an maximum likelihood (ML) tree and neighbor-joining (NJ) tree using MEGA X 40.
    MEGA
    suggested: (Mega BLAST, SCR_011920)
    Ancestral sequence reconstruction We used the amino acid-based marginal reconstruction implemented in the empirical Bayes approach in PAML 17 for ancestral sequence reconstruction.
    PAML
    suggested: (PAML, SCR_014932)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.03.04.975995v1 on bioRxiv